How to Collect Charity Case Studies Without the Pain

A practical guide to collecting charity case studies efficiently — from beneficiary consent to AI-powered write-ups that turn a 2-hour task into 5 minutes.

By Plinth Team

Every funder asks for case studies. Every charity knows they need them. And almost every small charity finds them agonisingly difficult to produce. It is not that the stories do not exist — they happen every day in frontline work. The problem is capturing them.

The typical process looks like this: a grant report is due in three weeks. The fundraiser emails five project workers asking for a case study. Two do not respond. One sends three paragraphs of jargon. Another identifies a willing beneficiary but cannot find time to sit down with them. The fifth provides something usable but it arrives the day before the deadline, without consent documentation, and needs heavy editing. In practice, charity staff can easily spend 2–4 hours producing a single funder-ready case study — and that figure does not include the weeks of chasing that precede it.

This guide covers how to build a reliable case study collection process, then introduces the approach that is transforming how small charities handle this task: recording a short conversation with a beneficiary and letting AI generate a funder-ready case study in minutes.

What you will learn:

  • Why case studies are so difficult for small charities to collect consistently
  • How to build a consent process that protects beneficiaries without creating barriers
  • What funders actually want from a case study (and what they do not)
  • How to use AI to turn a recorded conversation into a polished case study
  • A before-and-after comparison of the traditional vs AI-assisted process

Who this is for: Programme managers, fundraisers, monitoring and evaluation leads, and charity directors at organisations that need case studies for funder reports, grant applications, or public communications — and struggle to produce them consistently.


Why Are Case Studies So Hard to Collect?

The difficulty is not a single bottleneck. It is a chain of small frictions, each of which can stall the entire process.

Finding the Right Person

Not every beneficiary is suitable or willing to share their story. Programme staff need to identify someone whose experience genuinely illustrates the charity's impact, who is in a stable enough position to reflect on their journey, and who is comfortable being quoted (even anonymously). Many beneficiaries will decline, and the participation rate is often particularly low for services involving sensitive issues such as domestic abuse, addiction, or mental health.

Getting Meaningful Consent

Consent is non-negotiable, but many charities either over-complicate it (creating legal-looking forms that intimidate participants) or under-document it (relying on verbal agreement that would not satisfy a funder audit). The Charity Commission's guidance on safeguarding requires that consent for sharing personal stories be informed, specific, and documented — and that participants understand they can withdraw at any time.

Scheduling the Conversation

Frontline workers are busy. Beneficiaries may have chaotic lives. Finding a mutually convenient time for a sit-down interview — then protecting that time from cancellations and competing priorities — is a genuine operational challenge. In many organisations, the scheduling and rescheduling process accounts for a significant share of the total time spent on case study production.

Writing It Up

Even when the conversation happens, transforming raw notes or a recording into a coherent, compelling narrative takes skill and time. Many programme workers are excellent at their jobs but not confident writers. The result is either a clinical report that lacks emotional resonance or a piece that buries the key outcomes in unnecessary detail.

Getting It Approved

Once drafted, the case study typically needs to be reviewed by a manager, checked against the consent agreement, and sometimes shared back with the beneficiary for approval. Each review cycle adds days or weeks.

What Do Funders Actually Want From a Case Study?

Understanding what funders are looking for helps you collect the right information in the first place. Many charities over-produce — writing 1,500-word narratives when 500 words would be more effective.

Sector research on funder assessment practices, along with guidance from major funders including the National Lottery Community Fund and Comic Relief, suggests funders value:

ElementWhat funders wantCommon mistake
Situation before support2-3 sentences on the person's circumstancesToo vague ("they were struggling") or too detailed (full background history)
What you providedSpecific activities, duration, frequencyListing every service rather than what this person received
What changedObservable or measurable outcomesClaiming outcomes without evidence ("their life was transformed")
In their wordsA direct quote from the beneficiaryNo quotes, or quotes that sound written by a comms team
SafeguardingClear consent, appropriate anonymisationNo documentation, or real names used without explicit agreement
Brevity300–800 words1,500+ words that bury the key points

Grants managers reading hundreds of case studies a year consistently say the same thing: the ones that work are simple. Who was the person, what did you do, what changed, and how do we know. Everything else is padding.

How Should You Structure a Consent Process?

Good consent protects beneficiaries without creating a bureaucratic barrier that stops stories being collected at all. The best consent processes share four characteristics:

Simple language. The consent form should be readable by someone with no formal qualifications. Avoid legal jargon. Explain in plain English: "We would like to share your story to show funders how our service helps people. We will not use your real name unless you want us to."

Specific options. Let beneficiaries choose their level of comfort:

  • First name only, or fully anonymous
  • Verbal story only, or also a photograph
  • Shared with funders only, or also on the website
  • Time-limited consent (e.g. two years) or ongoing

Easy to withdraw. Make it clear that withdrawing consent is straightforward and has no consequences for their access to services.

Recorded at the right moment. Consent should be gathered when the beneficiary is engaged and positive — not at intake when they are anxious, and not months later when they have moved on. The natural moment is often at a review or ending session, when the person can reflect on their journey.

The Information Commissioner's Office (ICO) guidance on legitimate interests and consent under GDPR applies to all charity case studies. For sensitive personal data (health, ethnicity, criminal history), explicit consent is required and must be documented.

The Traditional Case Study Process: A Realistic Timeline

Here is what the process typically looks like for a small charity producing a case study the traditional way:

StageElapsed timeStaff time
Fundraiser requests case study from programme teamDay 110 minutes
Programme worker identifies suitable beneficiaryDays 2–1030 minutes (plus follow-up)
Consent obtained and documentedDays 5–1420 minutes
Interview scheduledDays 7–2115 minutes (plus rescheduling)
Interview conductedDays 14–2845–60 minutes
Notes written up into draft case studyDays 15–3560–90 minutes
Manager review and editsDays 20–4030 minutes
Beneficiary approval (if applicable)Days 25–5015 minutes
Total3–7 weeks3.5–5 hours

For a charity that needs four case studies per year across three programmes, that is 42–60 hours of staff time — roughly a full working week per year spent solely on case study production. For organisations with multiple funders requiring quarterly stories, the burden is far greater.

How Does AI Change Case Study Collection?

AI does not change the need for genuine beneficiary engagement. It does not fabricate stories or generate fictional case studies. What it does is collapse the most time-consuming parts of the process — the writing, structuring, and editing — into minutes rather than hours.

The AI-Assisted Process

Here is the same task using Plinth's AI case notes:

Step 1: Have the conversation (5 minutes). During a routine review session or at the end of a programme, the worker has a natural conversation with the beneficiary. They press record on their phone or tablet (with consent already obtained). The conversation is informal — "How were things when you first came to us? What has changed? What made the difference?"

Step 2: Take a photo (30 seconds). If the beneficiary consents to images, take a photo. This is not a professional shoot — a natural, well-lit photo taken on a phone is exactly what funders and websites need.

Step 3: Upload the recording (1 minute). The audio file is uploaded to Plinth. AI transcribes the conversation, identifies the beneficiary's key quotes, and separates the worker's questions from the beneficiary's responses.

Step 4: AI generates the case study (automatic). The AI produces a structured, funder-ready case study: situation before support, intervention provided, outcomes achieved, and a direct quote. It draws on the transcribed conversation and — if the beneficiary is an existing service user — any outcome data already recorded through surveys or case records. If a photo was provided, it is pulled into the narrative.

Step 5: Human review and approval (5–10 minutes). The programme worker or fundraiser reads the draft, adjusts the tone if needed, checks accuracy, confirms the consent level matches what is published, and approves.

Total staff time: 10–15 minutes. Total elapsed time: same day.

Before and After Comparison

AspectTraditional processAI-assisted process
Total staff time3.5–5 hours10–15 minutes
Elapsed time3–7 weeksSame day
Writing skill requiredHighLow (review only)
Consistency of outputVariableConsistent structure
Quotes from beneficiaryOften paraphrased or lostDirect from transcript
Outcome data includedManually compiledAutomatically pulled from records
Photo integratedSeparately attachedEmbedded in narrative
Number producible per month1–2 (realistically)10–20

The difference in output is stark. Charities that adopt this approach typically go from producing perhaps six case studies a year — always in a panic before a report is due — to building a library of thirty within three months. The quality improves too, because the case studies are based on actual conversations rather than reconstructed memories.

How Do You Build a Case Study Library Over Time?

The real power of making case study collection effortless is that you stop treating it as a special project and start treating it as a routine part of service delivery. Every exit interview, every six-month review, every celebration of a milestone becomes a potential case study.

Making It Part of the Workflow

The most effective approach is to build case study collection into existing touchpoints rather than creating new ones:

  • Exit interviews: When a beneficiary completes a programme, the review conversation is already happening. Adding five minutes of recorded reflection requires minimal additional time.
  • Milestone celebrations: When someone gets a job, moves into housing, or completes a course, there is a natural moment of positive reflection. Capture it.
  • Annual reviews: For longer-term service users, an annual review is an opportunity to document the journey so far.
  • Feedback sessions: If you already collect feedback through surveys, add an optional recorded element for those who want to share their story verbally.

Organising Your Case Studies

Once you have more case studies than you can hold in your head, you need a way to find the right one for the right purpose. Useful tags include:

  • Programme area (e.g. youth mentoring, food bank, housing)
  • Outcome demonstrated (e.g. employment, mental health improvement, housing stability)
  • Consent level (e.g. anonymous, first name, full name and photo)
  • Date collected (funders want recent stories — anything over 18 months old loses relevance)
  • Funder suitability (some stories suit specific funders' priorities)

Plinth's impact reporting organises case studies alongside quantitative outcome data, so when you are writing a funder report you can search by programme and outcome to find the right story to complement your numbers.

What Makes a Compelling Case Study?

Whether written by a human or drafted by AI, the best case studies share certain qualities:

Specificity over generality. "Sarah attended 14 weekly sessions and now manages her anxiety without medication" is more powerful than "our service helps people with their mental health."

The beneficiary's voice. Direct quotes carry more weight than third-person narration. This is where recorded conversations have a significant advantage — the AI transcribes the beneficiary's actual words rather than a staff member's recollection of what they said.

Honest complexity. The most credible case studies acknowledge that change is not always linear. A story that includes setbacks and challenges is more believable — and more useful to funders — than one that presents a seamless transformation.

Measurable change alongside personal narrative. Combining qualitative storytelling with quantitative data ("her wellbeing score improved from 3/10 to 7/10 over six months") gives funders both the human connection and the evidence they need.

How Do You Handle Sensitive Stories Responsibly?

Many charities work with people in vulnerable circumstances — domestic abuse survivors, people with addiction issues, those experiencing homelessness. Collecting case studies in these contexts requires additional care.

Timing matters. Do not ask someone to share their story while they are still in crisis. Wait until they are in a stable position and can reflect safely on their experience.

Anonymisation must be thorough. Changing a name is not always sufficient. If your charity operates in a small community, details such as age, number of children, or specific circumstances may be identifying. Check that the combination of details in the case study cannot identify the individual.

Trauma-informed approach. The conversation should be led by the beneficiary, not interrogated by the worker. Open questions ("What would you like people to know about your experience?") are preferable to directive ones ("Tell me about your lowest point").

Review by the beneficiary. For sensitive stories, always offer the beneficiary the opportunity to read and approve the final version before it is shared. This is not just good practice — it is a safeguarding requirement under the Charity Commission's guidance on protecting people who share their stories.

Withdrawal without consequence. If someone changes their mind after sharing their story, remove it promptly and completely. No questions, no pressure, no impact on their access to services.

The Care Quality Commission (CQC) and the Social Care Institute for Excellence (SCIE) both provide guidance on ethical storytelling that applies to charities working in health and social care contexts.

Frequently Asked Questions

How many case studies should we aim to collect each year?

A good target for most small charities is two to three per programme per quarter. This gives you enough material for funder reports, grant applications, and public communications without creating an excessive burden. With an AI-assisted process, this is comfortably achievable — it represents roughly one recorded conversation per programme per month.

Can we use AI-generated case studies in grant applications?

Yes. The AI generates a draft based on a real conversation with a real beneficiary. You review, edit, and approve it before use. The final product is your charity's case study — the AI is a drafting tool, no different from using a word processor. Funders care about authenticity and evidence, not whether the first draft was typed or generated.

What if a beneficiary is not comfortable being recorded?

Recording is not required. The worker can take notes during or after the conversation and enter them into the system. The AI can still generate a structured case study from written notes — the output will be slightly less rich (fewer direct quotes) but still significantly faster than writing from scratch. Always respect a beneficiary's preferences.

How do we handle consent for children and young people?

For children under 16, consent must be obtained from a parent or guardian as well as the young person themselves. For young people aged 16–17, they can generally consent for themselves, but good practice is to inform parents unless doing so would create a safeguarding risk. Use age-appropriate language in consent forms and ensure the young person genuinely understands how their story will be used.

What recording equipment do we need?

A smartphone is sufficient. Modern phone microphones produce clear enough audio for AI transcription, provided you are in a reasonably quiet environment. No specialist equipment is required. If you are recording video calls, most platforms (Zoom, Teams, Google Meet) have built-in recording features.

Can we use case studies across multiple funders?

Yes, provided the consent agreement allows it. This is why offering specific consent options matters — if a beneficiary consents to their story being shared "with funders," you can use it in multiple reports. If they consent to it being shared with a specific funder only, honour that restriction. Having a library of case studies with clear consent records means you can quickly identify which stories are suitable for which purpose.

How do we maintain quality when producing case studies quickly?

The AI produces consistently structured output, which actually improves quality compared to case studies written by different staff members with varying writing skills. The human review step is essential — never publish a case study without a staff member checking accuracy, tone, and consent compliance. But the review is checking a polished draft rather than creating from a blank page.

What if the AI gets something wrong in the case study?

AI-generated drafts are based on the transcribed conversation and existing records, so factual errors are uncommon. The most likely issues are misattributed quotes (if speaker identification is imperfect) or emphasis on the wrong outcomes. This is exactly what the human review step catches. Treat the AI output as a first draft — useful and time-saving, but always reviewed before use.

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Last updated: February 2026